Application of SMOTE and LSSVM with Various Kernels for Predicting Refactoring at Method Level

Kumar, Lov; Satapathy, Shashank Mouli; Krishna, Aneesh

doi:10.1007/978-3-030-04221-9_14

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…They ran a set of experiments on seven open-source software projects of different sizes (two large-scale projects and the others were medium and small projects), where the classes refactored were manually validated. In another study conducted by Kumar, Satapathy, and Krishna [42], LSSVM and SMOTE machine learning techniques were applied to five software projects to predict the opportunities for applying refactoring techniques at the method level.…”

Section: A Machine Learning-based Refactoring Predictionsmentioning

confidence: 99%

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

et al. 2023

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Refactoring is one of the most widely used techniques in practice to improve the quality of existing software. However, it is observed that refactoring does not continually improve all software quality attributes. Recent studies indicated that different refactoring techniques have significantly different, sometimes opposite, and conflicting effects on software quality attributes. In other words, there is contradictory evidence on the refactoring benefit. As a result, developers face challenges in selecting appropriate refactoring techniques when they use them to improve software quality. To the best of our knowledge, no study has investigated factors that may explain inconsistent or diverging results concerning the effect of refactoring techniques on software quality. Therefore, in this study, scenarios of using refactoring techniques factor have been identified, investigated, and thoroughly analyzed. Ten of the most commonly used refactoring techniques in practice have been chosen and individually applied in seven case studies of varying sizes (small, medium, and large). The Quality Model for Object-Oriented Design (QMOOD) is used to assess how refactoring techniques affect quality attributes. The findings provide strong evidence that this factor plays a significant role in producing the various effects of refactoring techniques on quality attributes. These findings can help software developers understand how to use refactoring techniques to improve software quality while taking this factor into account. The best scenario for using each refactoring technique to improve software system quality has been identified. The findings can provide guidelines for software developers to use refactoring techniques to improve the quality of software systems based on the best scenarios of using the refactoring techniques.

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Section: A Machine Learning-based Refactoring Predictionsmentioning

confidence: 99%

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

et al. 2023

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“…The main limitation is that they do not specify the "what" element, i.e., the refactorings that should be applied. So, the user has to choose the best refactoring based on their knowledge [Kumar et al 2018].…”

Section: Table 1 Research Questionsmentioning

confidence: 99%

Refactoring Recommendations with Machine Learning

Armijo

Camargo

2022

Anais Estendidos Do XXI Simpósio Brasileiro De Qualidade De Software (SBQS Estendido 2022)

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Refactoring is a well-known activity in software engineering that aims at changing a source-code snippet to improve structural quality attributes without changing the external behavior. It is known that manual refactoring is a common practice, but it is a time-consuming and error-prone activity. In literature it is possible to find approaches whose goal is to automatically recommend refactorings to software engineers. The goal is mainly to improve the productivity and internal quality of systems. However, we have observed that refactoring recommendations approaches are still immature, even those based on Machine Learning (ML). Thus, this PhD aims at researching how ML, along with explainability models, can be used to improve refactoring recommendations.

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“…Software refactoring modifies the internal structure of the software without altering its external functionality [8,9]. Moreover, software refactoring is employed to enhance the understandability, reduce the complexity, and increase the maintainability of the targeted software [10,11].…”

Section: Introductionmentioning

confidence: 99%

Software Refactoring Prediction Using SVM and Optimization Algorithms

Akour

Alenezi²,

Alsghaier³

2022

Processes

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Test suite code coverage is often used as an indicator for test suite capability in detecting faults. However, earlier studies that have explored the correlation between code coverage and test suite effectiveness have not addressed this correlation evolutionally. Moreover, some of these works have only addressed small sized systems, or systems from the same domain, which makes the result generalization process unclear for other domain systems. Software refactoring promotes a positive consequence in terms of software maintainability and understandability. It aims to enhance the software quality by modifying the internal structure of systems without affecting their external behavior. However, identifying the refactoring needs and which level should be executed is still a big challenge to software developers. In this paper, the authors explore the effectiveness of employing a support vector machine along with two optimization algorithms to predict software refactoring at the class level. In particular, the SVM was trained in genetic and whale algorithms. A well-known dataset belonging to open-source software systems (i.e., ANTLR4, JUnit, MapDB, and McMMO) was used in this study. All experiments achieved a promising accuracy rate range of between 84% for the SVM–Junit system and 93% for McMMO − GA + Whale + SVM. It was clear that added value was gained from merging the SVM with two optimization algorithms. All experiments achieved a promising F-measure range between the SVM–Antlr4 system’s result of 86% and that of the McMMO − GA + Whale + SVM system at 96%. Moreover, the results of the proposed approach were compared with the results from four well known ML algorithms (NB-Naïve, IBK-Instance, RT-Random Tree, and RF-Random Forest). The results from the proposed approach outperformed the prediction performances of the studied MLs.

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Application of SMOTE and LSSVM with Various Kernels for Predicting Refactoring at Method Level

Cited by 8 publications

References 10 publications

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

Revisiting Scenarios of Using Refactoring Techniques to Improve Software Systems Quality

Refactoring Recommendations with Machine Learning

Software Refactoring Prediction Using SVM and Optimization Algorithms

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